Screening for biomarkers of tuberous sclerosis complex-associated epilepsy: a bioinformatics analysis.

Background: The optimal biomarkers for early diagnosis, treatment, and prognosis of tuberous sclerosis complex (TSC)-associated epilepsy are not yet clear. This study identifies the crucial genes involved in the pathophysiology of TSC-associated epilepsy via a bioinformatics analysis. These genes may serve as novel therapeutic targets. Methods: Gene chip data sets (GSE62019 and GSE16969) comprising the data of patients with TSC-associated epilepsy and healthy control participants were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the GEO database were identified using the GEO2R gene expression analysis tool. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Ontology function, and protein-protein interaction (PPI) network analyses were then conducted. The results were analyzed using R language, and are presented in volcano plots, Venn diagrams, heatmaps, and enrichment pathway bubble charts. A gene set enrichment analysis (GSEA), was conducted to examine the KEGG pathways and crucial genes linked to TSC-associated epilepsy. The potential genes were compared with the genes listed in the Online Mendelian Inheritance in Man (OMIM) database and analyzed against the literature to determine their clinical significance. Finally, the expression of the key genes in the TSC-associated epilepsy mice cerebral cortex was examined through immunohistochemical staining. Results: The intersection of the GSE62019 and GSE16969 data sets revealed 151 commonly upregulated DEGs. The KEGG enrichment analysis indicated that these DEGs affected the occurrence and development of TSC-associated epilepsy by modulating complement and coagulation cascades, glycosaminoglycans in cancer, and extracellular matrix-receptor interactions. Four high-scoring clusters emerged, and podoplanin (PDPN) was identified as a key gene through the construction of a PPI network of the common DEGs using the Cytoscape software. A GSEA of the DEGs revealed that the common DEG PDPN was enriched in both data sets in pathways related to platelet activation, aggregation, and the glycoprotein VI (GPVI)-mediated activation cascade. Immunohistochemical staining revealed a significant elevation in PDPN expression in the cerebral cortex of mice with TSC-associated epilepsy. Conversely, the control group mice did not display any significantly positive neurons. Conclusions: The discovery of these crucial genes and signaling pathways extends understanding of the molecular processes underlying the development of TSC-associated epilepsy. Additionally, our findings may provide a theoretical basis for research into targeted clinical treatments.

Intravoxel incoherent motion diffusion-weighted imaging for early assessment of combined anti-angiogenic/chemotherapy for colorectal cancer liver metastases.

Background: To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the early assessment of colorectal cancer liver metastases (CRLM). Methods: A total of 34 patients with pathologically confirmed unresectable CRLM were enrolled. All participants uniformly received capecitabine and oxaliplatin (CAPOX) plus bevacizumab chemotherapy as standard first-line treatment for advanced colorectal cancer (CRC). Participants underwent 1.5-T conventional magnetic resonance imaging (MRI) and IVIM-DWI sequence scans with 9 b values (0 to 1,000 s/mm2) before treatment and at 3 weeks of treatment, and conventional MRI scans were performed at 6 and 12 weeks after the initial treatment. The IVIM-DWI parameters in the tumor target area were extracted using image post-processing software, including perfusion fraction (f), true diffusion coefficient (D), and false diffusion coefficient (D*). The response assessment was based on the Response Evaluation Criteria in Solid Tumors (RECIST) v. 1.1 by measuring the longest tumor diameter on dynamic contrast-enhanced (DCE) T1-weighted images. Results: According to the RECIST v. 1.1 criteria, the 34 participants were divided into a response group (n=16) and a non-response group (n=18). In the response group, the f value was significantly lowered (P=0.001) and the D value was significantly increased after treatment (P=0.002). In the non-response group, the D value was increased slightly after treatment (P=0.039), and there was no significant difference in the f value and the D* value. In addition, the f value at baseline was significantly greater in the response group than in the non-response group (0.221±0.033 vs. 0.175±0.040; P=0.001). The receiver operating characteristic (ROC) curve analysis showed that the percentage change of the f value obtained the largest area under the curve (AUC =0.797), and the AUC obtained by the Fisher's linear discriminant analysis (FLDA) method (Δf & ΔD combination) was 0.819. Conclusions: The IVIM-DWI parameters (f values and D values) provided effective assessment of the therapeutic effect of CAPOX combined with bevacizumab in patients with CRLM at an early stage, and the f value of the pre-treatment tumor area was shown to be useful for predicting the treatment response of patients.

Liquid biopsy using ascitic fluid and pleural effusion supernatants for genomic profiling in gastrointestinal and lung cancers.

BACKGROUND: Precision medicine highlights the importance of incorporating molecular genetic testing into standard clinical care. Next-generation sequencing can detect cancer-specific gene mutations, and molecular-targeted drugs can be designed to be effective for one or more specific gene mutations. For patients with special site metastases, it is particularly important to use appropriate samples for genetic profiling. This study aimed to determine whether genomic profiling using ASC and PE is effective in detecting genetic mutations. METHODS: Tissues, plasma, ascites (ASC) supernatants, and pleural effusion (PE) samples from gastrointestinal cancer patients with peritoneal metastasis and lung cancer patients with pleural metastasis were collected for comprehensive genomic profiling. The samples were subjected to next-generation sequencing using a panel of 59 or 1021 cancer-relevant genes panel. RESULTS: A total of 156 tissues, 188 plasma samples, 45 ASC supernatants, and 1 PE samples from 304 gastrointestinal cancer patients and 446 PE supernatants, 122 tissues, 389 plasma samples, and 45 PE sediments from 407 lung cancer patients were analyzed. The MSAF was significantly higher in ASC and PE supernatant than that in plasma ctDNA (50.00% vs. 3.00%, p < 0.0001 and 28.5% vs. 1.30%, p < 0.0001, respectively). The ASC supernatant had a higher actionable mutation rate and more actionable alterations than the plasma ctDNA in 26 paired samples. The PE supernatant had a higher total actionable mutation rate than plasma (80.3% vs. 48.4%, p < 0.05). The PE supernatant had a higher frequency of uncommon variations than the plasma regardless of distant organ metastasis. CONCLUSION: ASC and PE supernatants could be better alternative samples when tumor tissues are not available, especially in patients with only peritoneal or pleural metastases.

Development and External Validation of a Nomogram for Predicting Overall Survival in Stomach Cancer: A Population-Based Study.

OBJECTIVE: The study was to develop and externally validate a prognostic nomogram to effectively predict the overall survival of patients with stomach cancer. METHODS: Demographic and clinical variables of patients with stomach cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2007-2016 were retrospectively collected. Patients were then divided into the Training Group (n = 4,456) for model development and the Testing Group (n = 4,541) for external validation. Univariate and multivariate Cox regressions were used to explore prognostic factors. The concordance index (C-index) and the Kolmogorov-Smirnov (KS) value were used to measure the discrimination, and the calibration curve was used to assess the calibration of the nomogram. RESULTS: Prognostic factors including age, race, marital status, TNM stage, surgery, chemotherapy, grade, and the number of regional nodes positive were used to construct a nomogram. The C-index was 0.790 and the KS value was 0.45 for the Training Group, and the C-index was 0.789 for the Testing Group, all suggesting the good performance of the nomogram. CONCLUSION: We have developed an effective nomogram with ten easily acquired prognostic factors. The nomogram could accurately predict the overall survival of patients with stomach cancer and performed well on external validation, which would help improve the individualized survival prediction and decision-making, thereby improving the outcome and survival of stomach cancer.